Machine Learning
This page contains resources about Pattern Recognition and Machine Learning in general. More specific information is included in each subfield. Subfields See Category:Machine Learning for some of its subfields. Online Courses Video Lectures * Machine Learning by Andrew Ng - Coursera * Machine Learning by Pedro Domingos - Coursera * Neural Networks for Machine Learning by Geoffrey Hinton - Coursera * Practical Machine Learning by Jeff Leek - Coursera * NYU Course on Big Data, Large Scale Machine Learning by John Langford and Yann LeCun * Learning from Data by Yaser Abu-Mostafa * Introduction to Machine Learning by Barnabas Poczos and Alex Smola * Machine Learning by Nando de Freitas * Pattern Recognition by Fred A. Hamprecht (2011 2012 ) * Machine Learning and Pattern Recognition by Charles Sutton (Lecture notes ) * Pattern Recognition by P.S.Sastry - NPTEL * PASCAL Lecture Series - VideoLectures.Net Lecture Notes Introductory * CO395: Machine Learning by Maja Pantic - very introductory course * Prediction: Machine Learning and Statistics by Cynthia Rudin- very introductory course * SGN-2506: Introduction to Pattern Recognition by Jussi Tohka * CSCI1950-F: Introduction to Machine Learning by Erik Sudderth * CS 229: Machine Learning by Andrew Ng * CSC 411: Machine Learning and Data Mining by Aaron Hertzmann * CS 760: Machine Learning by David Page * Introduction To Machine Learning by David Sontag * CSC321: Introduction to Neural Networks and Machine Learning by Tijmen Tieleman - this might be a bit advanced for beginners * CSC2515: Introduction to Machine Learning by Geoffrey Hinton - this is very similar to the above * Introduction to Machine Learning Course by Sargur Srihari - this might be a bit advanced for beginners * COS 511: Foundations of Machine Learning by Rob Schapire * CS 2750 Machine Learning by Milos Hauskrecht * Introductory Applied Machine Learning by Victor Lavrenko and Nigel Goddard * Machine Learning and Pattern Recognition by Yann LeCun * Machine Learning by Tom Mitchell * Machine Learning by Tommi Jaakkola * Machine Learning by Andrew Zisserman * Pattern Recognition and Analysis by Rosalind W. Picard * Machine Learning by Carl Edward Rasmussen and Zoubin Ghahramani * Machine Learning: Pattern Recognition by Gwenn Englebienne Advanced * CS281: Advanced Machine Learning by Ryan Adams * CSC2535: Advanced Machine Learning by Geoffrey Hinton * Advanced Topics in Machine Learning by Andreas Krause * Advanced Topics in Machine Learning (Kernel Methods) by Arthur Gretton - Gatsby * Pattern Recognition by Ricardo Gutierrez-Osuna * Introduction to Pattern Recognition by Jason Corso * Pattern Recognition by Olga Veksler * Pattern Recognition by Charles Robertson * Pattern Recognition by Esa Alhoniemi Specialized * Statistical Machine Learning from Data by Samy Bengio * STA 4273H: Statistical Machine Learning by Ruslan Salakhutdinov * CS59000: Statistical Machine Learning by Alan Qi * Identification, Estimation, and Learning by Harry Asada * Unsupervised Learning by Zoubin Ghahramani * Probabilistic and Unsupervised Learning by Maneesh Sahani - Gatsby * Approximate Inference and Learning in Probabilistic Models by Maneesh Sahani - Gatsby * Reinforcement Learning by David Silver * Reinforcement Learning by Michael Herrmann * Pattern Recognition by Xia Hong * Pattern Recognition for Machine Vision by Bernd Heisele and Yuri Ivanov * CS 5785: Modern Analytics by Serge J. Belongie Books Practical * Witten, I. H., & Frank, E. (2005). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann. * Conway, D., & White, J. (2012). Machine Learning for Hackers. O'Reilly Media, Inc. * Brownlee, J. (2013). Clever Algorithms: Statistical Machine Learning Recipes. Jason Brownlee. * Mining, D. (2011). Concepts and Techniques. Jiawei Han and Micheline Kamber. 3rd Ed. Morgan Kaufmann. * Rajaraman, A., & Ullman, J. D. (2012). Mining of Massive Datasets. Cambridge University Press. * Battiti, R., & Brunato, M. (2014). The LION Way. Machine Learning Plus Intelligent Optimization. CreateSpace. * Elston, S. F. (2015). Data Science in the Cloud with Microsoft Azure Machine Learning and R. O'Reilly Media, Inc. * Lantz, B. (2015). Machine learning with R. 2nd Ed. Packt Publishing Ltd. * Raschka, S. (2015). Python Machine Learning. Packt Publishing Ltd. Introductory * Mitchell, T. M. (1997). Machine Learning. McGraw Hill. * Alpaydin, E. (2004). Introduction to Machine Learning. MIT Press. * Abu-Mostafa, Y. S., Magdon-Ismail, M., & Lin, H. T. (2012). Learning From Data. AMLBook. * James, G., Witten, D., & Hastie, T. (2014). An Introduction to Statistical Learning: With Applications in R. Advanced * Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. * Theodoridis, S., Pikrakis, A., Koutroumbas, K., & Cavouras, D. (2008). Pattern Recognition. 4th Ed. Academic Press. * Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern Classification. John Wiley & Sons. * Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press. * Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press. * Theodoridis, S. (2015). Machine Learning: A Bayesian and Optimization Perspective. Academic Press. Specialized * Webb, A. R. (2002). Statistical Pattern Recognition. 2nd Ed. John Wiley & Sons. * MacKay, D. J. (2003). Information Theory, Inference and Learning Algorithms. Cambridge University Press. * Kushner, H., & Yin, G. G. (2003). Stochastic Approximation and Recursive Algorithms and Applications (Vol. 35). 2nd Ed. Springer Science & Business Media. * Taylor,J. S. & Cristianini, N. (2004). Kernel Methods for Pattern Analysis. Cambridge University Press. * Williams, C. K., & Rasmussen, C. E. (2006). Gaussian Processes for Machine Learning. MIT Press. * Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Friedman, J., & Tibshirani, R. (2009). The Elements of Statistical Learning (Vol. 2, No. 1). 2nd Ed. New York: Springer. * Bühlmann, P., & Van De Geer, S. (2011). Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer Science & Business Media. * Barber, D. (2012). Bayesian Reasoning and Machine Learning. Cambridge University Press. * Bubeck, S. & Cesa-Bianchi, N. (2012). Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. Foundations and Trends® in Machine Learning, 5(1), 1-122. Now Publishers. * Jebara, T. (2012). Machine Learning: Discriminative and Generative (Vol. 755). Springer Science & Business Media. Software * PRMLT - MATLAB Toolbox for the book of PRML by C. Bishop * pmtk3 - Probabilistic Modeling Toolkit for MLPP book by Murphy in Matlab/Octave (3rd edition) * pyprobml - Python code for MLPP book by K. Murphy * BRMLtoolbox - MATLAB and Julia code for the BRML book by D. Barber * PyBRML - Python code for the BRML book by D. Barber * OpenML * BigML - Prediction and Analytics tasks under 16MB are free * Neural Network Toolbox - MATLAB * Torch7 - a scientific computing framework for Machine Learning algorithms (based on Lua) * Lush - an OOP language for large-scale numerical and graphic applications (based on Lisp) * Pylearn2 - A Machine Learning research library * scikit-learn - Python * mlpy - Python * Orange - Data Visualization and Analysis * Matlab Machine Learning Toolboxes * mloss.org. Machine Learning open source software * April-ANN - A Pattern Recognizer In Lua with ANNs * Weka- Data mining software in Java * MLTK - Machine Learning Toolkit in Java * OpenNN * Bayesian Modeling and Monte Carlo Methods * Lightspeed Matlab Toolbox * MCML - broad range support for Monte Carlo methods to implement Machine Learning applications Datasets * UCI Machine Learning Repository - a large collection of standard datasets for testing learning algorithms * DeepLearning.Net - a list of datasets that can be used for benchmarking Deep Learning algorithms * MLdata See also *System Identification *Predictive Learning vs. Representation Learning Other Resources *Video Tutorials - Youtube channel of 'Mathematical Monk' * AML (Github) - A curated list of Machine Learning frameworks, libraries and software (by language). *Comparison of software toolkits *Software for Data Mining, Analytics, Data Science, and Knowledge Discovery - KDnuggets * Metacademy - List of concepts in Machine Learning * A Course in Machine Learning by Hal Daumé III - textbook * Courses on Statistical Pattern Recognition - summary of 33 courses * FastML - List of Machine Learning courses online * Machine Learning Surveys - List of literature surveys, reviews, and tutorials on Machine Learning and related topics * Machine Learning on Google+ - online community * The Shape of Data - Data Mining and Machine Learning blog * Data Mining & Machine Learning - a mindmap diagram comparing the two areas * Basic Glossary of Machine Learning Category:Machine Learning